Classification Of A Person's Multiple Intelligence Based On Posting On Twitter Using Spearman's Rank Correlation Coefficient

Feb 27, 2025 by ADMIN 125 views

Unlocking the Secrets of Multiple Intelligence: A Study on Classifying Compound Intelligence using Twitter Data and Spearman's Rank Correlation Coefficient

Introduction

In today's fast-paced world, self-discovery and personal growth have become essential aspects of an individual's journey. Intelligence, a crucial component of self-discovery, plays a vital role in problem-solving and adapting to the environment. However, each person has different dominant intelligences, and recognizing and honing these abilities can lead to more directed self-control and self-development. But how can we identify these dominant intelligences? This is where the concept of multiple intelligence comes into play.

Understanding Multiple Intelligence

Proposed by Howard Gardner, multiple intelligence theory suggests that each individual has variations in ability and learning styles. This theory has been widely accepted and has led to a deeper understanding of human cognition. The nine types of intelligence, as identified by Gardner, are:

Musical-Rhythmic Intelligence: The ability to recognize and create musical patterns.
Visual-Spatial Intelligence: The ability to visualize and understand spatial relationships.
Verbal-Linguistic Intelligence: The ability to understand and use language effectively.
Logical-Mathematical Intelligence: The ability to reason and solve mathematical problems.
Bodily-Kinesthetic Intelligence: The ability to use body movements to solve problems.
Interpersonal Intelligence: The ability to understand and interact with others.
Intrapersonal Intelligence: The ability to understand oneself and one's emotions.
Naturalistic Intelligence: The ability to understand and appreciate the natural world.
Spiritual Intelligence: The ability to understand and connect with a higher power.

Classifying Multiple Intelligence using Twitter Data

In this study, we explored the possibility of classifying multiple intelligence using Twitter data. Twitter, a social media platform, allows users to express their thoughts and feelings in the form of tweets. By analyzing these tweets, we can gain insights into an individual's dominant intelligences. Our research process involved sentiment analysis and classification of intelligence types using the Sentistory method in the Apache Spark framework.

Methodology

Our study involved analyzing 20 Twitter user accounts based on the nine types of intelligence. We used the Spearman ranking correlation coefficient to calculate the correlation between manual test results and the results obtained from the system. The significance level was set at 0.05, and the Spearman value was 0.700.

Results

The results of our study showed an average accuracy of 72%. This accuracy was obtained by calculating the correlation between manual test results and the results obtained from the system using the Spearman ranking correlation coefficient. Our findings suggest that Twitter data can be used to classify multiple intelligence with a reasonable level of accuracy.

Additional Analysis and Explanation

The use of social media as a source of data offers a modern way to explore individual personal characters. By paying attention to the uploaded tweet, we can draw conclusions about the more dominant types of intelligence. Sentiment analysis methods such as Sentistory allow large data processing and provide insight into emotions or nuances that are reflected in each tweet.

Implications and Future Directions

Our study has several implications for education, career development, and improving the quality of life. Understanding multiple intelligences can help individuals recognize their potential and take steps to develop according to their intelligence. Furthermore, this approach can be used to identify areas of improvement and provide targeted interventions.

In the current digital era, understanding multiple intelligences can be utilized in a broader context. Going forward, further research is needed to expand sample data and implement new techniques to maximize the accuracy of intelligence predictions based on posts on social media. This is an important step in the continuing era of information and technological developments.

Conclusion

In conclusion, our study demonstrates the potential of using Twitter data to classify multiple intelligence. The results of our study show that Twitter data can be used to identify dominant intelligences with a reasonable level of accuracy. This approach has several implications for education, career development, and improving the quality of life. As we continue to navigate the digital era, understanding multiple intelligences will become increasingly important for personal growth and development.
Frequently Asked Questions: Classifying Multiple Intelligence using Twitter Data

Q: What is multiple intelligence, and why is it important?

A: Multiple intelligence is a theory proposed by Howard Gardner that suggests that each individual has variations in ability and learning styles. Understanding multiple intelligence is important because it can help individuals recognize their potential and take steps to develop according to their intelligence.

Q: How can Twitter data be used to classify multiple intelligence?

A: Twitter data can be used to classify multiple intelligence by analyzing the tweets posted by an individual. Sentiment analysis methods such as Sentistory can be used to process large amounts of data and provide insight into emotions or nuances that are reflected in each tweet.

Q: What are the benefits of using Twitter data to classify multiple intelligence?

A: The benefits of using Twitter data to classify multiple intelligence include:

Improved accuracy: Twitter data can provide a more accurate picture of an individual's dominant intelligences.
Increased efficiency: Analyzing Twitter data can be faster and more efficient than traditional methods.
Cost-effective: Using Twitter data can be more cost-effective than traditional methods.

Q: What are the limitations of using Twitter data to classify multiple intelligence?

A: The limitations of using Twitter data to classify multiple intelligence include:

Limited sample size: Twitter data may not be representative of an individual's entire personality or intelligence.
Biased data: Twitter data may be biased towards certain topics or interests.
Lack of context: Twitter data may lack context, making it difficult to interpret.

Q: How can the accuracy of multiple intelligence classification be improved?

A: The accuracy of multiple intelligence classification can be improved by:

Increasing the sample size: Analyzing more Twitter data can provide a more accurate picture of an individual's dominant intelligences.
Using more advanced algorithms: Using more advanced algorithms such as machine learning can improve the accuracy of multiple intelligence classification.
Combining multiple data sources: Combining multiple data sources such as Twitter data and traditional methods can provide a more accurate picture of an individual's dominant intelligences.

Q: What are the implications of using Twitter data to classify multiple intelligence?

A: The implications of using Twitter data to classify multiple intelligence include:

Improved education: Understanding multiple intelligence can help individuals recognize their potential and take steps to develop according to their intelligence.
Improved career development: Understanding multiple intelligence can help individuals choose careers that are in line with their dominant intelligences.
Improved quality of life: Understanding multiple intelligence can help individuals make informed decisions about their personal and professional lives.

Q: What are the future directions for research on multiple intelligence classification using Twitter data?

A: Future directions for research on multiple intelligence classification using Twitter data include:

Expanding the sample size: Analyzing more Twitter data can provide a more accurate picture of an individual's dominant intelligences.
Using more advanced algorithms: Using more advanced algorithms such as machine learning can improve the accuracy of multiple intelligence classification.
Combining multiple data sources: Combining multiple data sources such as Twitter data and traditional methods can provide a more accurate picture of an individual's dominant intelligences.